Python

GeoPolars extends the Polars DataFrame library for use with geospatial data. The main goals of GeoPolars are faster multithreaded operations than GeoPandas and better data interoperability.

Here is a hands-on coding lesson teaching geospatial concepts and Python packages for querying, accessing and processing geospatial data.

Here are some tools for working with Google Earth Engine from a Jupyter development environment which provide a foundation for new libraries on top of the Google Earth Engine Python API.

This article aims to demonstrate how to semantically segment aerial imagery using a U-Net model defined in TensorFlow.

This book will introduce you to the methods required for spatial programming. It focuses on building your core programming techniques while helping you carry out various geospatial tasks.

The geeml Python package makes it easier to extract satellite data from Google Earth Engine using parallel processing and the Google Earth Engine high volume endpoint.

This workshop presents and exemplifies a subset of GRASS GIS toolsets for satellite imagery data processing and analysis in combination with other core modules and add-ons.

Check out this four-part series demonstrating how to use machine learning for detecting changes in land cover. Open source libraries and tools are used for this tutorial.

Take a look here for some free resources on learning QGIS, Python, PyQGIS, Google Earth Engine and GDAL/OGR. You can pick courses that suit your current level of expertise.

Check here for a Python script for creating an animated GIF, given a shapefile with time-annotated vector objects (e.g., building footprints and construction year).